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All Journal International Journal of Research in Vocational Studies (IJRVOCAS)
Hsien-Wei Tseng
Tamkang University
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Deep Learning and N8N-Based Soybean Defect Recognition and Traceable Quality Grading System Rafie Hamizan Al Hafiz; Pola Risma; Hsien-Wei Tseng; Chun-Chieh Fan
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 6 No. 2 (2026): IJRVOCAS - August
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v6i2.532

Abstract

Soybean is an important food commodity whose market value is strongly influenced by seed quality. However, quality inspection in many small and medium agro-industries is still performed manually, making the process slow, subjective, and poorly documented. This study develops an automated soybean inspection system that combines deep-learning-based defect recognition with traceability-supported quality reporting through workflow automation. A YOLOv11n model was trained on a self-collected dataset of 344 annotated images covering five classes, namely Intact, Broken, Skin Damaged, Spotted, and Immature, which was expanded to 1,032 images through augmentation. The model runs on a Raspberry Pi 5 inspection station, where accumulated detection results are converted into a defect rate that determines the quality grade of each batch. An n8n workflow then forwards every inspection result to the Gemini 2.5 Flash large language model to generate a narrative quality-control report, and each batch is stored in an SQLite database that can be accessed through a history viewer to support traceability. Validation results show a precision of 92.73%, a recall of 90.71%, an mAP@0.50 of 96.54%, and an mAP@0.50–0.95 of 88.67%. Functional testing demonstrates that the system produces consistent grades, accumulates detections from multiple captures into a single batch, and generates reports automatically, while still producing a rule-based report when the language model service is unavailable. The proposed system therefore offers an automated, low-cost, and traceability-supported approach to soybean quality inspection that is suitable for deployment on edge devices.
Co-Authors Chun-Chieh Fan Pola Risma Rafie Hamizan Al Hafiz